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Robust histogram-based feature engineering of time series data

机译：基于稳健的直方图的时间序列特征工程

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摘要

Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better understanding of the processes that are being monitored. In this paper we propose a novel generic histogram-based method for feature engineering of time series data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with first derivatives, and finally calculating histograms. By doing all of those steps the goal is three-fold: achieve invariance to different factors, good modeling of the data and preform significant feature reduction. This method was applied to the AAIA Data Mining Competition 2015, which was concerned with recognition of activities carried out by firefighters by analyzing body sensor network readings. By doing that we were able to score the third place with predictive accuracy of about 83%, which was about 1% worse than the winning solution.
机译：如今，定期收集数据无处不在。收集和分析的最广泛使用的数据类型是财务数据和传感器读数。各种企业已经意识到，财务时间序列分析是一种强大的分析工具，可以带来竞争优势。同样，传感器网络会生成时间序列，如果对其进行了适当的分析，则可以更好地了解所监视的过程。在本文中，我们提出了一种新颖的基于直方图的通用方法来进行时间序列数据的特征工程。预处理阶段包括几个步骤：对时间序列数据进行去声纳分析，使用一阶导数对变化速度进行建模，最后计算直方图。通过执行所有这些步骤，目标是三方面的：实现对不同因素的不变性，对数据进行良好的建模以及对显着特征进行简化。该方法已应用于2015年的AAIA数据挖掘竞赛中，该竞赛旨在通过分析人体传感器网络读数来识别消防员的活动。通过这样做，我们能够以约83％的预测准确性获得第三名，这比获胜解决方案差了约1％。

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